knitr::opts_chunk$set(echo = TRUE)
# Load libraries for homework problems
library(tidyverse)
library(gt)
# Read in the data
abpm_wide <- read_csv('data/abpm_wide_synthetic.csv')Ambulatory blood pressure monitoring (ABPM) is a technique for assessing a person’s blood pressure (BP). ABPM is conducted with a special device that consists of a BP cuff worn on the participant’s arm and attached to a small recording device worn on the belt. The ABPM device is usually worn for 24 hours, and it records BP periodically (usually at 15-minute or 30-minute intervals). One of the benefits of ABPM is measuring BP during routine daily activities and during sleep instead of in clinical settings. A previous study by Hermida et al. (2018) found that, among all BP derive risk factors, asleep Systolic BP is the most associated with cardiovacular disease events.
I have modified the New York times data to include information about state’s population levels. The data are described below:
c("sleep_time" = "Time of falling asleep",
"awake_time" = "Time of waking up",
"age" = "Participant age, years",
"sex" = "Participant sex at birth",
"race" = "Participant race",
"educ" = "Participant education at exam time",
"smoke" = "Participant smoking status at exam time",
"sbp_0 - sbp_23" = "Systolic BP, hours since midnight",
"dbp_0 - dbp_23" = "Diastolic BP, hours since midnight",
"hr_0 - hr_23" = "Heart rate, hours since midnight"
) %>%
enframe() %>%
gt(rowname_col = "name") %>%
tab_stubhead(label = 'Variable name') %>%
cols_label(value = 'Variable description') %>%
cols_align('right') %>%
tab_source_note("BP = blood pressure") %>%
tab_header(title = 'Dictionary for synthetic ABPM data')| Dictionary for synthetic ABPM data | |
|---|---|
| Variable name | Variable description |
| sleep_time | Time of falling asleep |
| awake_time | Time of waking up |
| age | Participant age, years |
| sex | Participant sex at birth |
| race | Participant race |
| educ | Participant education at exam time |
| smoke | Participant smoking status at exam time |
| sbp_0 - sbp_23 | Systolic BP, hours since midnight |
| dbp_0 - dbp_23 | Diastolic BP, hours since midnight |
| hr_0 - hr_23 | Heart rate, hours since midnight |
| BP = blood pressure | |
The data (cv19) are printed below:
abpm_wideConvert the smoke variable in abpm_wide into a factor and exclude participants with missing data for sleep_time, awake_time, and smoke.
Notes:
The factor labels should be
read_rds('solutions/01_solution.rds')Pivot the data into a longer format.
Notes:
Create a column named id that uniquely identifies each row of abpm_wide.
Pivot the data into a longer form such that each id has a column for sbp, dbp, and hr. You may need to use pivot_longer, then separate, then pivot_wider.
Drop all rows with missing data for sbp, dbp, or hr
read_rds('solutions/02_solution.rds')Create an factor variable called awake that has values of 'Yes' when participants are awake and 'No' when asleep.
Notes:
There are two scenarios that are relevant:
sleep time is less than awake time
sleep time is greater than awake time.
read_rds('solutions/03_solution.rds')Notes:
read_rds('solutions/04_solution.rds')